Best local AI models for NVIDIA Quadro K5000M

The largest models that fit fully

The 30 largest of the 81 models that fit; every smaller model in the catalog fits too. Best quant means the highest quality compression whose weights and 4k context both sit inside the memory.

Close, but only with CPU offload

These need more than the card holds at their smallest practical quant, so part of the model runs from system memory (figures assume 32 GB of it). They work, several times slower.

How to read this

The NVIDIA Quadro K5000M is a mobile workstation graphics card equipped with 4 GB of GDDR5 memory. This dedicated video memory determines the maximum size of the artificial intelligence models you can run entirely on the hardware. When running models locally, the entire active architecture and its working memory must fit within this physical limit to maintain acceptable processing speeds.

To fit larger models into the 4 GB memory space, developers use quantization. The quant column indicates the compression level applied to the model weights. For example, a Q4_K_M quant uses approximately four bits per parameter, while a Q8_0 quant uses eight bits. Higher quantization levels like Q8_0 preserve more original model accuracy but require more memory. Lower levels like Q4_K_M allow larger parameter counts to fit within your hardware limits.

For models that exceed the 4 GB video memory limit, you can use CPU offload. This technique splits the model layers between your graphics card and your system RAM. We assume a standard system configuration with 32 GB of system RAM for these scenarios. Offloading allows you to run larger architectures, but transferring data between the system memory and the graphics card over the system bus reduces processing speeds significantly.

Several high quality models can run completely within the local video memory. The Lumina-Next or Lumina-Image 2.0 model at 5B parameters fits using a Q4_K_M quant which consumes 3.7 GB of memory. The CogVideoX 5B model also fits at Q4_K_M using 3.7 GB. For vision tasks, DeepSeek-VL2 at 4.5B parameters fits using a Q5_K_M quant which uses 3.8 GB of memory. The Phi-3.5-vision model at 4.2B parameters fits at Q5_K_M using 3.6 GB.

Text and audio models also fit well within the native memory. The Qwen3 4B, Gemma 3 4B, Gemma 4 E4B, MiniCPM 3 4B, Danube 3 4B, and Fish Speech 1.5 or OpenAudio S1 models all fit at 4B parameters using a Q6_K quant which consumes 3.9 GB. The Phi-4-mini-instruct and Phi-3.5 Mini models at 3.8B parameters use a Q6_K quant requiring 3.7 GB. Small models like SmolLM3 3B and Replit Code v1.5 3B can run at a high quality Q8_0 quant using 3.8 GB.

When using CPU offload, you can run much larger models at the cost of speed. The Mistral 7B model requires 5.7 GB of video memory at Q4_K_M and needs 7.7 GB of system RAM. The Qwen2.5 7B, OLMo 2 7B, Falcon 3 7B, Command R7B, and OpenHermes 2.5 models all require 5.1 GB of video memory at Q4_K_M and 7.1 GB of system RAM. Stable Diffusion XL requires 4.1 GB of video memory at FP8 or optimized settings along with 6.1 GB of system RAM.

You must monitor your context window size when running these models. The memory numbers listed here are calculated using a standard 4k context window. If you increase the context length to process longer documents or longer chat histories, the memory requirements will increase. This extra memory usage can push a model past the 4 GB threshold and trigger slow system RAM offloading.